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J Child Psychol Psychiatry. 2016 Aug;57(8):927-37. doi: 10.1111/jcpp.12559. Epub 2016 Apr 19.

Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion.

Author information

1
Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA.
2
San Francisco School of Medicine, University of California, San Francisco, CA, USA.
3
Information Sciences Institute, University of Southern California, Los Angeles, CA, USA.
4
Department of Health Sciences, Northeastern University, Boston, MA, USA.
5
Center for Autism and the Developing Brain, Weill Cornell Medical College, New York, NY, USA.

Abstract

BACKGROUND:

Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely used ASD screening and diagnostic tools.

METHODS:

The data consisted of Autism Diagnostic Interview-Revised (ADI-R) and Social Responsiveness Scale (SRS) scores for 1,264 verbal individuals with ASD and 462 verbal individuals with non-ASD developmental or psychiatric disorders, split at age 10. Algorithms were created via a robust ML classifier, support vector machine, while targeting best-estimate clinical diagnosis of ASD versus non-ASD. Parameter settings were tuned in multiple levels of cross-validation.

RESULTS:

The created algorithms were more effective (higher performing) than the current algorithms, were tunable (sensitivity and specificity can be differentially weighted), and were more efficient (achieving near-peak performance with five or fewer codes). Results from ML-based fusion of ADI-R and SRS are reported. We present a screener algorithm for below (above) age 10 that reached 89.2% (86.7%) sensitivity and 59.0% (53.4%) specificity with only five behavioral codes.

CONCLUSIONS:

ML is useful for creating robust, customizable instrument algorithms. In a unique dataset comprised of controls with other difficulties, our findings highlight the limitations of current caregiver-report instruments and indicate possible avenues for improving ASD screening and diagnostic tools.

KEYWORDS:

Autism; diagnosis; machine learning; screening

PMID:
27090613
PMCID:
PMC4958551
DOI:
10.1111/jcpp.12559
[Indexed for MEDLINE]
Free PMC Article

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